{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 时间序列数据统计—滑动窗口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 窗口函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-01    0.557684\n",
      "2017-01-02    2.200996\n",
      "2017-01-03    3.598553\n",
      "2017-01-04    3.607741\n",
      "2017-01-05    3.614044\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "ser_obj = pd.Series(np.random.randn(1000), \n",
    "                    index=pd.date_range('20170101', periods=1000))\n",
    "ser_obj = ser_obj.cumsum()\n",
    "print(ser_obj.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-01    0.557684\n",
      "2017-01-02    2.200996\n",
      "2017-01-03    3.598553\n",
      "2017-01-04    3.607741\n",
      "2017-01-05    3.614044\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(ser_obj.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rolling [window=5,center=False,axis=0]\n"
     ]
    }
   ],
   "source": [
    "r_obj = ser_obj.rolling(window=5)\n",
    "print(r_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-01          NaN\n",
      "2017-01-02          NaN\n",
      "2017-01-03          NaN\n",
      "2017-01-04          NaN\n",
      "2017-01-05     2.715804\n",
      "2017-01-06     3.449078\n",
      "2017-01-07     3.872749\n",
      "2017-01-08     4.038992\n",
      "2017-01-09     4.106373\n",
      "2017-01-10     4.482935\n",
      "2017-01-11     4.835012\n",
      "2017-01-12     5.342875\n",
      "2017-01-13     5.614054\n",
      "2017-01-14     5.859185\n",
      "2017-01-15     5.889861\n",
      "2017-01-16     5.573034\n",
      "2017-01-17     4.657874\n",
      "2017-01-18     4.277580\n",
      "2017-01-19     4.250566\n",
      "2017-01-20     3.879157\n",
      "2017-01-21     3.985028\n",
      "2017-01-22     4.173051\n",
      "2017-01-23     3.711651\n",
      "2017-01-24     2.796506\n",
      "2017-01-25     2.092927\n",
      "2017-01-26     1.584232\n",
      "2017-01-27     1.228735\n",
      "2017-01-28     1.398981\n",
      "2017-01-29     1.575194\n",
      "2017-01-30     2.121598\n",
      "                ...    \n",
      "2019-08-29   -46.217440\n",
      "2019-08-30   -45.933571\n",
      "2019-08-31   -46.071790\n",
      "2019-09-01   -46.036403\n",
      "2019-09-02   -45.971552\n",
      "2019-09-03   -46.183122\n",
      "2019-09-04   -46.057991\n",
      "2019-09-05   -45.931402\n",
      "2019-09-06   -45.853265\n",
      "2019-09-07   -45.694977\n",
      "2019-09-08   -45.409338\n",
      "2019-09-09   -45.162806\n",
      "2019-09-10   -45.192550\n",
      "2019-09-11   -45.407836\n",
      "2019-09-12   -45.563197\n",
      "2019-09-13   -45.480304\n",
      "2019-09-14   -45.453541\n",
      "2019-09-15   -45.106606\n",
      "2019-09-16   -44.345104\n",
      "2019-09-17   -44.118564\n",
      "2019-09-18   -43.759602\n",
      "2019-09-19   -43.379807\n",
      "2019-09-20   -43.068156\n",
      "2019-09-21   -43.069352\n",
      "2019-09-22   -42.345210\n",
      "2019-09-23   -42.045101\n",
      "2019-09-24   -41.799906\n",
      "2019-09-25   -41.760096\n",
      "2019-09-26   -41.831898\n",
      "2019-09-27   -42.351763\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(r_obj.mean())\n",
    "\n",
    "# 验证：\n",
    "# 前5个数据的均值\n",
    "# print(ser_obj[0:5].mean())\n",
    "\n",
    "# 1-6个数据的均值\n",
    "# print(ser_obj[1:6].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23705e9a080>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UIYhdu2KpnyIiIiIiEibBk514ntfO87zlnuctP6p1VtfneTYMd+YMYEsAV6++prpC9LRq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\nZ//+2g169erNq7JbGQNzO6dP6zDftGlh+3aoVQvSpOG93mk5eFDLRvo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JzVrp66tJRYCu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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23705e9dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图查看\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "ser_obj.plot(style='r--')\n",
    "ser_obj.rolling(window=10).mean().plot(style='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-01          NaN\n",
      "2017-01-02          NaN\n",
      "2017-01-03     2.715804\n",
      "2017-01-04     3.449078\n",
      "2017-01-05     3.872749\n",
      "2017-01-06     4.038992\n",
      "2017-01-07     4.106373\n",
      "2017-01-08     4.482935\n",
      "2017-01-09     4.835012\n",
      "2017-01-10     5.342875\n",
      "2017-01-11     5.614054\n",
      "2017-01-12     5.859185\n",
      "2017-01-13     5.889861\n",
      "2017-01-14     5.573034\n",
      "2017-01-15     4.657874\n",
      "2017-01-16     4.277580\n",
      "2017-01-17     4.250566\n",
      "2017-01-18     3.879157\n",
      "2017-01-19     3.985028\n",
      "2017-01-20     4.173051\n",
      "2017-01-21     3.711651\n",
      "2017-01-22     2.796506\n",
      "2017-01-23     2.092927\n",
      "2017-01-24     1.584232\n",
      "2017-01-25     1.228735\n",
      "2017-01-26     1.398981\n",
      "2017-01-27     1.575194\n",
      "2017-01-28     2.121598\n",
      "2017-01-29     2.306638\n",
      "2017-01-30     3.002676\n",
      "                ...    \n",
      "2019-08-29   -46.071790\n",
      "2019-08-30   -46.036403\n",
      "2019-08-31   -45.971552\n",
      "2019-09-01   -46.183122\n",
      "2019-09-02   -46.057991\n",
      "2019-09-03   -45.931402\n",
      "2019-09-04   -45.853265\n",
      "2019-09-05   -45.694977\n",
      "2019-09-06   -45.409338\n",
      "2019-09-07   -45.162806\n",
      "2019-09-08   -45.192550\n",
      "2019-09-09   -45.407836\n",
      "2019-09-10   -45.563197\n",
      "2019-09-11   -45.480304\n",
      "2019-09-12   -45.453541\n",
      "2019-09-13   -45.106606\n",
      "2019-09-14   -44.345104\n",
      "2019-09-15   -44.118564\n",
      "2019-09-16   -43.759602\n",
      "2019-09-17   -43.379807\n",
      "2019-09-18   -43.068156\n",
      "2019-09-19   -43.069352\n",
      "2019-09-20   -42.345210\n",
      "2019-09-21   -42.045101\n",
      "2019-09-22   -41.799906\n",
      "2019-09-23   -41.760096\n",
      "2019-09-24   -41.831898\n",
      "2019-09-25   -42.351763\n",
      "2019-09-26          NaN\n",
      "2019-09-27          NaN\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(ser_obj.rolling(window=5, center=True).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
